JP7408066B2 - Measurement of radiant thermal energy density in the grid area of the build surface - Google Patents

Description

[0001] CROSS REFERENCE TO RELATED APPLICATIONS This application was filed on August 1, 2017, each entitled "Systems And Methods For Measuring Energy Input During An Additive Manufacturing Operation" U.S. Provisional Patent Application No. 62/540, No. 016, No. 62/633,487 filed on February 21, 2018, and No. 62/643,457 filed on March 15, 2018, the disclosures of which are incorporated herein by reference. Incorporated herein by reference in its entirety for all purposes.

[0002] Additive manufacturing, or the sequential assembly or construction of parts by a combination of the addition of materials and applied energy, takes many forms and currently exists in many specific implementations and embodiments. Additive manufacturing can be performed using any of a variety of processes involving the formation of three-dimensional parts of virtually any shape. The various processes generally involve sintering, hardening, or melting liquid, powder, or granular raw materials layer by layer using ultraviolet light, high power lasers, or electron beams, respectively. Unfortunately, established processes for determining the quality of parts manufactured in this manner are limited. Traditional quality assurance testing typically involves post-process measurements of mechanical, geometric, or metallurgical properties of a part, which often results in part failure. Destructive testing is an accepted method of verifying the quality of a part because it allows for the close examination of various internal features of the part, but such tests cannot be applied to production parts for obvious reasons. Therefore, a method to nondestructively and accurately verify the mechanical, geometric, and metallurgical properties of production parts produced by additive manufacturing is desired.

[0003] The described embodiments relate to additive manufacturing, which involves the use of an energy source in the form of a transfer region of intense thermal energy. When this thermal energy causes physical melting of the added material, these processes are commonly known as welding processes. In the welding process, material is added incrementally and continuously and melted by an energy source in a manner similar to fusion welding. Exemplary welding processes suitable for use with the described embodiments include processes that use a scanning energy source with a powder bed, and wire-fed processes that use an arc, laser, or electron beam as the energy source. .

[0004] When the added material is in the form of a layer of powder, the scanning energy source welds the area of the powder layer after each incremental layer of powder material is added successively to the part being built. melts the incrementally added powders, producing a moving melt area (hereinafter referred to as the melt pool), so that upon solidification, they are removed from the previously added successively under the new layer. Becomes part of the melted and solidified layer forming the part being built. Avoid at least small variations in melt pool size and temperature when using the melt pool to solidify the part, since additive machining processes can be time consuming and involve many passes of the melt pool That can be difficult. Embodiments described herein reduce or minimize discontinuities caused by variations in melt pool size and temperature. Note that due to the high speed movement of heating elements and the complex patterns required to form three-dimensional structures, additive manufacturing processes may be driven by one or more processors associated with computer numerical control (CNC).

[0005] The overall purpose of the described embodiments is to apply optical sensing techniques to additive manufacturing processes, such as, for example, quality estimation, process control, or both. Optical sensors can be used to track the evolution of in-process physical phenomena by tracking the evolution of relevant in-process physical variables. As used herein, optics can include portions of the electromagnetic spectrum including near infrared (IR), visible, and near ultraviolet (UV). Generally, the optical spectrum is considered to span wavelengths from 380 nm to 780 nm. However, near UV and near IR can extend to wavelengths as short as 1 nm and as long as 3000 nm, respectively. Sensor readings collected from optical sensors can be used to determine process quality metrics (IPQM). One such IPQM is thermal energy density (TED), which helps characterize the amount of energy applied to different areas of a part.

[0006] TED is a metric that is sensitive to user-defined laser powder bed fusion process parameters such as laser power, laser speed, and hatch spacing. This metric can be used for analysis using IPQM comparison with a baseline dataset. The resulting IPQM can be calculated for each scan and displayed graphically or in three dimensions using a point cloud. Additionally, IPQM comparisons with baseline data sets indicative of manufacturing defects can be used to generate control signals for process parameters. In some embodiments, thermal energy density can be determined for individual portions of each scan if detailed thermal analysis is desired. In some embodiments, thermal energy data from multiple scans can be divided into separate grid regions of the grid, each grid region of one layer or a predefined number of layers. can reflect the total amount of energy received.

[0007] An additive manufacturing method is disclosed, the method comprising: generating multiple scans of an energy source across a build surface; and using an optical sensor to monitor the build surface between each of the multiple scans. determining the area of the build surface traversed during the plurality of scans; based on the amount of energy radiated and the area of the build surface traversed by the plurality of scans; determining a thermal energy density for an area of the build surface traversed by the plurality of scans; mapping the thermal energy density to one or more locations on the build surface; and then adjusting subsequent scans of the energy source across or proximate one or more locations of the build surface.

[0008] An additive manufacturing method is disclosed, the method comprising: generating a scan of an energy source across a build surface; using an optical sensor to monitor the powder bed; emitted from the powder bed during the scan; determining the area associated with the scan; determining the thermal energy density of the area of the scan based on the amount of energy radiated and the area of the scan; and determining the thermal energy density as a density value. and then adjusting subsequent scans of the energy source across the build surface.

[0009] An additive manufacturing method is disclosed, the method comprising: performing an additive manufacturing operation using an energy source; and detecting sensor data associated with a photodiode during scanning of the energy source across a powder bed. receiving drive signal data indicating that the energy source is powered on; and using the energy source drive signal data collected when the energy source is powered on. dividing the sensor data into a plurality of sample sections, each sample section corresponding to a portion of the scan; determining; and identifying one or more portions of the part most likely to contain manufacturing defects based on the thermal energy density of each of the plurality of sample sections.

[0010] An additive manufacturing method is disclosed, the method comprising: generating a plurality of scans of an energy source across a build surface; and determining a grid area including the plurality of scans, the grid area being a grid area. using an optical sensor to generate a sensor reading during each of the plurality of scans; using the sensor reading to generate an area radiated from the build surface during the plurality of scans; determining a total amount of energy radiated; calculating a thermal energy density associated with the grid area based on the total amount of energy radiated and the grid area; , determining that the method is characterized by a thermal energy density outside the range of thermal energy density values; and then adjusting the output of the energy source.

[0011] An additive manufacturing method is disclosed, the method comprising: defining a portion of a build surface as a grid including a plurality of grid regions, each having a grid region area; generating scans; using an optical sensor to generate sensor readings during each of the plurality of scans; and generating a respective portion of the plurality of sensor readings for each of the plurality of scans; mapping to each one of the plurality of grid regions; for each of the plurality of grid regions: summing the sensor readings mapped to each grid region; based on the summed sensor readings and the grid region area; the grid-based thermal energy density associated with one or more of the plurality of grid regions is characterized by a thermal energy density outside the range of thermal energy density values; and then adjusting the output of the energy source.

[0012] The present disclosure will be better understood by the following detailed description in conjunction with the accompanying drawings, where like reference numerals indicate like structural elements.

1 is a schematic illustration of an optical sensing device used in an additive manufacturing system with an energy source, in this particular example interpreted to be a laser beam; FIG. 1 is a schematic diagram of an optical sensing device used in an additive manufacturing system with an energy source, interpreted to be an electron beam in this particular example; FIG. FIG. 3 illustrates a sample scan pattern used in an additive manufacturing process. 1 is a flowchart depicting a method for identifying portions of a part most likely to contain manufacturing defects. FIG. 3 illustrates data related to a step-by-step process for using thermal energy density to identify portions of parts most likely to contain manufacturing defects. FIG. 3 illustrates data related to a step-by-step process for using thermal energy density to identify portions of parts most likely to contain manufacturing defects. FIG. 3 illustrates data related to a step-by-step process for using thermal energy density to identify portions of parts most likely to contain manufacturing defects. FIG. 3 illustrates data related to a step-by-step process for using thermal energy density to identify portions of parts most likely to contain manufacturing defects. FIG. 3 illustrates data related to a step-by-step process for using thermal energy density to identify portions of parts most likely to contain manufacturing defects. FIG. 3 illustrates data related to a step-by-step process for using thermal energy density to identify portions of parts most likely to contain manufacturing defects. FIG. 3 illustrates data related to a step-by-step process for using thermal energy density to identify portions of parts most likely to contain manufacturing defects. FIG. 3 illustrates data related to a step-by-step process for using thermal energy density to identify portions of parts most likely to contain manufacturing defects. 2 is a flowchart detailing a method for completing an IPQM evaluation using scanlet data separation. FIG. 3 illustrates data related to a step-by-step process for using thermal energy density to identify portions of parts most likely to contain manufacturing defects. FIG. 3 illustrates data related to a step-by-step process for using thermal energy density to identify portions of parts most likely to contain manufacturing defects. FIG. 3 illustrates data related to a step-by-step process for using thermal energy density to identify portions of parts most likely to contain manufacturing defects. FIG. 3 illustrates data related to a step-by-step process for using thermal energy density to identify portions of parts most likely to contain manufacturing defects. FIG. 3 illustrates data related to a step-by-step process for using thermal energy density to identify portions of parts most likely to contain manufacturing defects. FIG. 3 illustrates data related to a step-by-step process for using thermal energy density to identify portions of parts most likely to contain manufacturing defects. FIG. 3 shows test results comparing IPQM metrics to post-process metallography. FIG. 3 shows test results comparing IPQM metrics to post-process metallography. FIG. 3 shows test results comparing IPQM metrics to post-process metallography. FIG. 3 illustrates an alternative process in which data recorded by optical sensors, such as non-imaging photodetectors, can be processed to characterize an additive manufacturing build process. A visual depiction of how multiple scans can contribute to the power introduced in individual grid areas. A visual depiction of how multiple scans can contribute to the power introduced in individual grid areas. A visual depiction of how multiple scans can contribute to the power introduced in individual grid areas. A visual depiction of how multiple scans can contribute to the power introduced in individual grid areas. 1 illustrates an exemplary turbine blade suitable for use in the described embodiments; FIG. 10 illustrates an example manufacturing configuration in which 25 turbine blades can be manufactured simultaneously on a build surface 1006. FIG. FIG. 1OB is a cross-sectional view of the different layers of the configuration depicted in FIG. 10B. 1OB is another cross-sectional view of a different layer of the configuration depicted in FIG. 10B. FIG. 2 is a cross-sectional view of the base portion of the turbine blade. FIG. 3 is another cross-sectional view of the base portion of the turbine blade. 2 is a photograph showing the difference in surface consistency between two different base parts. FIG. 3 illustrates the thermal energy density of parts associated with a number of different builds. FIG. 4 illustrates an example of how thermal energy density can be used to control the operation of a component using field measurements. FIG. 4 illustrates an example of how thermal energy density can be used to control the operation of a component using field measurements. FIG. 4 illustrates an example of how thermal energy density can be used to control the operation of a component using field measurements. 2 is another power density graph highlighting various physical effects caused by setting the energy source too far from the process window. FIG. 3 shows how the size and shape of the melt pool can vary according to laser power and scan speed settings. FIG. 3 illustrates how grids can be dynamically generated to characterize and control additive manufacturing operations. FIG. 3 illustrates how grids can be dynamically generated to characterize and control additive manufacturing operations. FIG. 3 illustrates how grids can be dynamically generated to characterize and control additive manufacturing operations. FIG. 3 illustrates how grids can be dynamically generated to characterize and control additive manufacturing operations. FIG. 3 illustrates how grids can be dynamically generated to characterize and control additive manufacturing operations. FIG. 3 illustrates how grids can be dynamically generated to characterize and control additive manufacturing operations. FIG. 16 illustrates an example control loop 1600 for establishing and maintaining feedback control of additive manufacturing operations. FIG. 3 shows a normal distribution of powder across the build plate. FIG. 6 illustrates how the thickness of the resulting powder layer can vary when insufficient amounts of powder are collected and spread across the build plate by the recoater arm. A black and white photograph of a build plate where a short feed of powder only partially covers the nine workpieces placed on the build plate. FIG. 4 illustrates how the detected thermal energy density differs substantially when the energy source scans across all nine workpieces using the same input parameters.

[0038] FIG. 1A illustrates an embodiment of an additive manufacturing system that uses one or more optical sensing devices to determine thermal energy density. Thermal energy density is sensitive to changes in process parameters such as energy source power, energy source speed, hatch spacing, etc. The additive manufacturing system of FIG. 1A uses a laser 100 as an energy source. Laser 100 emits a laser beam 101 that passes through partially reflective mirror 102 and enters scanning and focusing system 103, which then projects the beam onto a small area 104 on work platform 105. In some embodiments, the working platform is a powder bed. Due to the high material temperature, optical energy 106 is emitted from small area 104.

[0039] In some embodiments, scanning and focusing system 103 may be configured to collect a portion of optical energy 106 emitted from beam interaction region 104. Partially reflective mirror 102 can reflect optical energy 106 as indicated by optical signal 107. The optical signal 107 may be interrogated by a plurality of on-axis optical sensors 109, each receiving a portion of the optical signal 107 via a series of additional partially reflective mirrors 108. Note that in some embodiments, the additive manufacturing system may include only one on-axis optical sensor 109 with a fully reflective mirror 108.

[0040] The collected optical signal 107 has some attenuation due to some attenuation of the signal 107 after passing through multiple optical elements, such as a partially reflective mirror 102, a scanning and focusing system 103, and a series of additional partially reflective mirrors 108. Note that it may not have the same spectral content as the light energy 106 emitted from the active region 104. Each of these optical elements has unique transmission and absorption properties that can result in varying amounts of attenuation and limiting certain portions of the spectrum of energy emitted from beam interaction region 104. The data generated by the on-axis optical sensor 109 may correspond to the amount of energy imparted to the work platform.

[0041] Examples of on-axis optical sensors 109 include, but are not limited to, pyrometers and light-to-electrical signal converters (i.e., photodetectors) such as photodiodes. Optical sensors can also include spectrometers and low-speed or high-speed cameras operating in the visible, ultraviolet, or infrared frequency spectrum. The on-axis optical sensor 109 is in a reference frame that moves with the beam, i.e. it sees all the areas that the laser beam contacts and the work platform 105 that the laser beam 101 contacts as it scans across the work platform 105. Optical signals 107 can be collected from all areas of the area. The optical energy 106 collected by the scanning and focusing system 103 follows a path approximately parallel to the laser beam, so the sensor 109 can be considered an on-axis sensor.

[0042] In some embodiments, the additive manufacturing system may include an off-axis sensor 110 that is in a stationary reference frame with respect to the laser beam 101. These off-axis sensors 110 have a given field of view 111 that can be very narrow, or can surround the entire work platform 105. Examples of these sensors include, but are not limited to, pyrometers, photodiodes, spectrometers, fast or slow cameras operating in the visible, ultraviolet, or IR spectral ranges, and the like. Off-axis sensors 110 that are not aligned with the energy source are considered off-axis sensors. The off-axis sensor 110 actively excites or "pings" the deposit with a single laser beam to measure or predict the mechanical properties or mechanical integrity of the deposit during the build. It may also be a sensor that combines a range of physical measurement modalities, such as a laser ultrasound sensor that uses an interferometer to measure the resulting ultrasound or "ringing" of the structure. Laser ultrasonic sensor/interferometer systems can be used to measure the elastic properties of materials, which can, for example, determine the porosity of the material and other material properties. Additionally, defect formation that causes vibrations in the material can be measured using a laser ultrasound/sensor interferometer system.

[0043] Additionally, the recoater arm 112, which is the mechanical device that spreads the powder, may include a contact sensor 113. These sensors may be accelerometers, vibration sensors, etc. Finally, there may be other types of sensors 114. These may include contact sensors such as thermocouples to measure macrothermal fields, and acoustic emission sensors that can detect cracks and other metallurgical phenomena that occur in the deposit during the build. These contact sensors may be utilized during the powder addition process to characterize the operation of the recoater arm 112. Data collected by on-axis optical sensor 109 and off-axis sensor 110 can be used to detect process parameters related to recoater arm 112. Therefore, surface inhomogeneities of the spread powder can be detected and addressed by the system. Rough surfaces resulting from variations in the powder spreading process can be characterized by the contact sensor 113 to predict possible problem areas or non-uniformities in the resulting part.

[0044] In some embodiments, the peaks of the powder spread may be fused by the laser beam 101, resulting in a subsequent layer of powder with corresponding peaks. At some point, the peaks may contact and damage the recoater arm 112, which may result in further powder non-uniformity. Thus, embodiments of the present invention can detect non-uniformities in the spread powder before the non-uniformities occur in the build area on the work platform 105. Many variations, modifications, and substitutions will be recognized by those skilled in the art.

[0045] In some embodiments, on-axis optical sensor 109, off-axis sensor 110, contact sensor 113, and other sensors 114 may be configured to generate in-process raw sensor data. In other embodiments, on-axis optical sensor 109, off-axis optical sensor 110, contact sensor 113, and other sensors 114 may be configured to process data and generate low-dimensional sensor data.

[0046] In some embodiments, a computer 116, including a processor 118, a computer readable medium 120, and an I/O interface 122, is provided and includes appropriate system components of an additive manufacturing system to collect data from various sensors. is combined with The data received by computer 116 may include in-process raw sensor data and/or low-dimensional sensor data. Processor 118 may use in-process raw sensor data and/or low-dimensional sensor data to determine the power of laser 100 and control information including coordinates for work platform 105. In other embodiments, computer 116, including processor 118, computer readable medium 120, and I/O interface 122, may provide control of various system components. Computer 116 can transmit, receive, and monitor control information related to laser 100, work platform 105, and recoater arm 112 to control and adjust each component's respective process parameters.

[0047] Processor 118 may be used to perform calculations using data collected by the various sensors to generate in-process quality metrics. In some embodiments, data generated by on-axis optical sensor 109 and/or off-axis sensor 110 can be used to determine thermal energy density during the build process. Control information related to movement of the energy source across the build surface can be received at the processor. The processor can then use the control information to correlate data from the on-axis optical sensor 109 and/or the off-axis optical sensor 110 with the corresponding position. By combining this correlation data, thermal energy density can be calculated. In some embodiments, the thermal energy density and/or other metrics are used by the processor 118 to determine laser power, laser speed, Control signals for hatch spacing and other process parameters can be generated. In this way, problems that might otherwise ruin production parts can be remedied. In embodiments where multiple parts are produced at once, process parameters can be quickly modified in response to metrics falling outside the desired range to ensure that adjacent parts receive too much or too little energy from the energy source. You can prevent it from going too far.

[0048] In some embodiments, I/O interface 122 may be configured to transmit collected data to a remote location. The I/O interface may be configured to receive data from a remote location. The received data may include baseline data sets, historical data, post-process inspection data, and classification data. The remote computing system can use data sent from the additive manufacturing system to calculate in-process quality metrics. The remote computing system can send information to I/O interface 122 in response to certain in-process quality metrics.

[0049] For an electron beam system, FIG. 1B shows possible configurations and placement of the sensor. Electron beam gun 150 produces an electron beam 151 that is focused by electromagnetic focusing system 152 and then deflected by electromagnetic deflection system 153, resulting in a finely focused and targeted electron beam 154. Electron beam 154 creates a hot beam material interaction zone 155 on workpiece 156. Light energy 158 is emitted from workpiece 156 and may be collected by a series of optical sensors 159, each having its own field of view 160, which can be isolated locally into interaction region 155 or encompass the entire workpiece 156. Additionally, optical sensor 159 can have its own tracking and scanning system that can track electron beam 154 as it moves across workpiece 156.

[0050] Regardless of whether sensor 159 has optical tracking, sensor 159 can be comprised of a pyrometer, a photodiode, a spectrometer, and a high-speed or low-speed camera operating in the visible, UV, or IR spectral regions. Sensor 159 actively excites or "pings" the deposit with a single laser beam and uses a laser interferometer to measure or predict the mechanical properties or mechanical integrity of the deposit during the build. It may also be a sensor that combines a range of physical measurement modalities, such as a laser ultrasound sensor that uses a laser to measure the resulting ultrasound or "ringing" of a structure. Additionally, there may be a contact sensor 113 on the recoater arm. These sensors may be accelerometers, vibration sensors, etc. Finally, there may be other types of sensors 114. These may include contact sensors such as thermocouples to measure macrothermal fields, and acoustic emission sensors that can detect cracks and other metallurgical phenomena that occur in the deposit during the build. In some embodiments, one or more thermocouples may be used to calibrate the temperature data collected by sensor 159. It is noted that the sensors described in connection with FIGS. 1A and 1B can be used in the described manner to characterize the performance of additive manufacturing processes involving continuous material accumulation.

[0051] FIG. 2 shows a possible hatch pattern for scanning an energy source across a powder bed. At 200, an area of the workpiece is processed by an energy source that scans along a long path length in alternating directions. In this embodiment, a hatch spacing 204 is shown between the first scan 206 and the second scan 208. At 202, the area of the workpiece is divided into smaller checkerboards 214 that can be scanned sequentially from left to right and top to bottom by a first scan 210 and a second scan 212. In other embodiments, the scan order of individual checkerboards can be randomized. Several hatch patterns may be utilized in connection with the additive manufacturing process disclosed herein. Many variations, modifications, and substitutions will be recognized by those skilled in the art.

[0052] FIG. 3 depicts a flowchart illustrating an example process 300 for determining thermal energy density and identifying portions of a part most likely to contain manufacturing defects using data generated by an additive manufacturing system. It shows. Data generated by on-axis optical sensor 109 and off-axis optical sensor 110 can be used alone or in combination to determine thermal energy density. At 302, a raw photodiode data trace is received. A raw photodiode data trace may be generated using, for example, voltage data generated by a sensor in response to detection of emitted thermal energy. At 304, a portion of the raw photodiode trace corresponding to a particular scan scan _i is identified. In some embodiments, individual photodiode data traces may be separated from the rest of the sensor readings by reference to energy source drive signal data (drive signals involved in operation and actuation of the energy source). At 306, the area under the raw photodiode data trace (hereinafter pdon _i ) of scan _i is determined. In some embodiments, pdon _i can represent the integrated photodiode voltage. In some embodiments, pdon _i represents the average reading of the photodiode during scan _i . At 308, part p associated with scan _i is identified. The part identified at 308 may also have an associated area A _p of the part. These two values can be determined by correlating pdon _i with the energy source position data as described above. The process may calculate a total scan count at 310. At 312, a length L _i associated with scan _i can be determined. L _i can be calculated using equation (1), where x1 _i , y1 _i and x2 _i , y2 _i represent the start and end positions of scan _i :

[0053] At 314, the total length Lsum _p of all scans used in manufacturing the part may be determined. Lsum _p for a part can be determined by summing the length L _i of each scan associated with the part. At 316, the prorated area A _i of the scan may be determined. A _i can be calculated using equation (2):

[0054] At 316, a prorated thermal energy density (TED) TED _i for the i-th scan may be determined. TED _i is an example of a set of reduction process features. TED is calculated using raw photodiode data. From this raw sensor data, the TED calculation extracts features of the dimensionality reduction process from the raw sensor data. TED _i is sensitive to all user-defined laser powder bed fusion process parameters, such as laser power, laser speed, hatch spacing, etc. TED _i can be calculated using equation (3):

[0055] For the purposes of this description, "reduced order" means: data compression, i.e., less data for features compared to the raw data; data reduction, i.e., process metrics or other performance systematic analysis of raw data that yields an index; data aggregation, i.e., clustering of data into discrete groups and smaller sets of variables that characterize the clustering as opposed to the raw data itself; data transformation, i.e., transformation rules or Mathematical manipulation of data to linearly or non-linearly map the raw data to another variable space of lower dimensionality using algorithms; alternatively, reducing data density, reducing the number of dimensions of the data, reducing data size; Refers to one or more aspects of the transformation of data into another reduced space, or other related techniques that have a net effect, all of which occur at the same time.

[0056] TED _i can be used for analysis during comparison of process quality metrics (IPQM) with a baseline dataset. The resulting IPQM can be calculated for each scan. At 318, the IPQM quality baseline dataset and the calculated TED _i may be compared. In areas of the part where the difference between the calculated TED and the baseline data set exceeds a threshold, those areas can be identified as potentially containing one or more defects, and/or near real-time Further processing can be performed on the data set to improve defects caused by variations in TED from the baseline data set. In some embodiments, portions of the part that may contain defects may be identified using a classifier. The classifier can group results as either nominal or non-nominal and can be represented in graphical and/or text-based media. Classifiers are: statistical classification, both single and multivariable; heuristic-based classifiers; expert system-based classifiers; look-up table-based classifiers; classifiers based solely on upper or lower bounds of control; confidence A classifier that works in conjunction with one or more statistical distributions that can establish nominal versus non-nominal thresholds based on interval and/or degrees of freedom considerations; or feature data, whether implicit or explicit. Multiple classification methods can be used, including, but not limited to, other classification schemes that can identify whether a set of is nominal or non-nominal. For the purposes of this description, "nominal" means a set of process results that were within predefined specifications, and the post-process measured attributes of parts thus manufactured are considered acceptable. applicable to value regimes, or other quantitative, semi-quantitative, objective, or subjective methodologies for establishing "acceptable" components. Additional explanation regarding the IPQM classification is provided in U.S. Patent Application No. 15/282,822, filed September 30, 2016, the disclosure of which is incorporated herein by reference in its entirety for all purposes. incorporated into the book.

[0057] It should be appreciated that the particular steps illustrated in FIG. 3 provide a particular method of collecting data and determining thermal energy density in accordance with embodiments of the invention. Other orders of steps may be performed according to alternative embodiments. Also, the individual steps shown in FIG. 3 may include multiple substeps that may be performed in various orders as appropriate for the individual steps. Additionally, additional steps can be added or existing steps can be deleted depending on the specific application. Many variations, modifications, and substitutions will be recognized by those skilled in the art.

[0058] FIGS. 4A-4H illustrate the steps used in process 300 to determine the TED and identify any portions of the part that are likely to contain defects. FIG. 4A corresponds to step 302 and shows the raw photodiode signal 402 for a given scan length. The x-axis 450 shows time in seconds and the y-axis 460 shows photodiode voltage. In some embodiments, optical measurements can alternatively or additionally be made with a pyrometer. Signal 402 is the raw voltage of the photodiode. The rising edges 404 and falling edges 406 of the photodiode signal 402 as well as the scattering and fluctuations 408 of the signal while the laser is on can be clearly seen. Data is collected at a given number of samples/second. Fluctuations 408 in photodiode signal 402 may be caused by fluctuations in the powder melting on the powder bed. For example, one of the small valleys in the photodiode signal 402 may be caused by energy absorbed by larger particles in the particle bed transitioning from a solid state to a liquid state. Generally, the number of data points in a given segment of the photodiode signal between rising and falling events may be related to the scan period and sampling rate.

[0059] FIG. 4B shows the raw photodiode signal 402 and laser drive signal 410. The laser drive signal 410 shown in FIG. 4B may be generated using energy source drive signal data, in this case the laser drive signal 410, or a command signal to direct the laser on and off at a particular scan length. Photodiode signal 402 is superimposed on laser drive signal 410. Rising edges 412 and falling edges 414 of laser drive signal 410 correspond to rising edges 404 and falling edges 406 of photodiode signal 402 . The data shown in FIG. 4B may be used in step 304 to identify the portion of the raw photodiode signal 402 that corresponds to the scan. In some embodiments, laser drive signal 410 is approximately 0V when the laser is off and approximately 5V when the laser is on. Step 304 is accomplished by isolating all data associated with photodiode signals where the laser drive signal 410 exceeds a certain threshold, e.g. 4.5V, and excluding from analysis all data where the laser is below this threshold. obtain.

[0060] FIG. 4C illustrates one embodiment of step 306 that includes determining an area 416 under the raw photodiode signal 402. In some embodiments, the area under the curve may be calculated using equation (4):

The integrated photodiode voltage 418 can be used to determine pdon _i for the TED _i calculation.

[0061] FIG. 4D shows the location of scans 420 and total scan counts 424 for the part. Both values can be used to determine the TED corresponding to the scan position of the part. FIG. 4E shows the rendered area of the part of interest 426. FIG. 4E also shows a number of additional parts 428 and a witness coupon 430. All components in FIG. 4E are depicted placed on a powder bed 432.

[0062] FIG. 4F shows traces associated with a portion of the photodiode data and laser drive signal data corresponding to four scans that can be used on the remainder of the photodiode data to determine the total sample count 434. . The total sample count can be used to calculate the total scan length L Sum _p of the part. The total sample count is determined by summing the laser on periods 436. In some embodiments, the total laser-on period and the average velocity of the scanning energy source during the laser-on period can be used to determine the total scan length.

[0063] After the scan data is collected, the TED of each layer can be calculated from the TED associated with each laser scan and displayed in graph 440 shown in FIG. 4G. Graph 440 shows TED values located within nominal region 442 and non-nominal region 444. The TED region is divided by a baseline threshold 438. In this way, layers of the part that are likely to contain defects can be easily identified. Further analysis can then focus on layers with non-nominal TED values.

[0064] FIG. 4H illustrates how a point cloud 446 can be used to display the TED values for each scan in three dimensions. Point cloud 446 indicates the location in three-dimensional space of the TED values from nominal region 442 and non-nominal region 444 by displaying the non-nominal value as a different color or intensity than the nominal value. Non-nominal values indicate areas of the part that are most likely to contain manufacturing defects, such as porosity due to keyhole formation or voids due to lack of fusion. In some embodiments, the system can generate and send control signals that change one or more process parameters based on the TED.

[0065] FIG. 5 depicts a flowchart illustrating an example process 500 for determining thermal energy density and identifying portions of a part most likely to contain manufacturing defects using data generated by an additive manufacturing system. It shows. Data generated by on-axis optical sensor 109 and off-axis optical sensor 110 can be used alone or in combination to determine thermal energy density. At 502, photodiode time series data can be collected. Photodiode time series data may be generated using, for example, voltage data associated with a sensor. At 504, laser drive time series data is collected. Laser drive time data may be related to additional process parameters such as laser power, laser speed, hatch spacing, xy position, etc. The process at 506 can slice the photodiode time series data by dropping the portion of the photodiode time series data that corresponds to the portion of the laser drive time series data that indicates the laser off state. In some embodiments, the laser drive signal is about 0V when the laser is off and about 5V when the laser is on. The process at 506 can isolate all data where the laser drive signal exceeds a certain threshold, such as 4.5V, and exclude from analysis all data where the laser is below this threshold. In some embodiments, the sample sequence data may include a photodiode signal that periodically drops to about 0.2V because the laser is just on and there are times when the laser is heating the material.

[0066] The process at 506 outputs only laser-on-photodiode data 508. The process at 510 can use laser-on-photodiode data to convert time series data to sample series data. The process at 510 divides the laser-on-photodiode data into "N" sample sections. The use of 20 sample sections is intended to provide an example of one embodiment of the invention. Any number of sample sections of varying accuracy/resolution can be used. In some embodiments, a set of sample sections can be referred to as a scanlet 520, since multiple scanlets 520 are generally required to constitute a single scan. The process at 512 may count (516) the number of samples. The process at 514 can render the area of the part that is irradiated by the laser. In some embodiments, the number of pixels in the display associated with the lasered part can be used to determine the lasered part area 518. In other embodiments, the number of scans and data related to process parameters can be used to calculate area. At 522, the process normalizes the scanlet data using the total sample count lasered part area 518 and scanlet data 520. In the illustrated embodiment, scanlet metric data 524 is the thermal energy density of the portion of the part associated with each scanlet. In some embodiments, scan data may also be categorized by scan type. For example, additive manufacturing machines can utilize scans with different characteristics. In particular, contour scans, or scans designed to finish the external surface of a part, can be significantly more powerful than scans designed to sinter the internal areas of the part. Therefore, by separating data by scan type, you can get more consistent results. In some embodiments, identification of scan type may be based on scan intensity, scan duration, and/or scan location. In some embodiments, the scan type can be identified by correlating the detected scan with a scan directed by a scan plan associated with the part being built.

[0067] Process 528 then receives the baseline scanlet metric data and thermal energy density and outputs an IPQM quality assessment 530. IPQM quality assessment 530 may be used to identify portions of the part that are most likely to contain manufacturing defects. Process 528 may include a classifier as previously described herein. In addition to the methods and systems described above, process 528 can compare candidate data, such as scanlet metric data 524, to baseline scanlet metric data using a Mahalanobis distance. In some embodiments, the Mahalanobis distance for each scanlet can be calculated using baseline scanlet metric data. Although the embodiments disclosed in connection with FIG. 5 discuss the use of lasers as energy sources, in light of the above teaching that lasers can be replaced with electron beams or other suitable energy sources, for example. It will be apparent to those skilled in the art that many modifications and variations are possible.

[0068] Certain steps illustrated in FIG. 5 provide a certain method for determining thermal energy density and identifying portions of a part most likely to contain manufacturing defects, in accordance with another embodiment of the invention. I want you to understand. Other orders of steps may be performed according to alternative embodiments. For example, alternative embodiments of the invention may perform the above steps in a different order. Also, the individual steps shown in FIG. 5 may include multiple substeps that may be performed in various orders as appropriate for the individual steps. Additionally, additional steps may be added or removed depending on the particular application. Many variations, modifications, and substitutions will be recognized by those skilled in the art.

[0069] FIG. 6A shows photodiode time series data 602. Photodiode time series data may be collected from various on-axis or off-axis sensors, as shown in FIGS. 1 and 2. The x-axis 602 shows time in seconds and the y-axis 604 shows the voltage produced by the sensor. The voltage generated by the sensor is related to the energy emitted from the build surface that may impinge on the sensor or sensors. A sample 606 is shown on the trace of photodiode time series data 602. FIG. 4B illustrates a process at 506 in which photodiode data is associated with a laser drive signal.

[0070] FIG. 6B shows laser-on-photodiode data. The x-axis 608 represents the number of samples and the y-axis 610 represents the voltage of the raw sensor data. The voltage drop 612 is included in the analysis because although the voltage is much lower, the laser is still actively contributing to the heating of the material.

[0071] FIG. 6C shows sample sequence data for the laser on the photodiode described in connection with step 510. The twenty sample sections 620 can have any size. The 20 samples correspond to a laser movement speed of 1000 mm/s and a laser movement distance of approximately 400 μm. The noise of the XY signal itself is approximately 150 μm. In some embodiments, in sections of less than 20 samples, such as 2 sample sections, the measured distance and noise may be in such a ratio that the location of a point cannot be confidently determined. In some embodiments, a limit of 50 samples can be used for spatial resolution of less than 1 mm. Therefore, the number of samples for segmenting data needs to be in the range 20≦N≦50 for 50 KHz data with a laser movement speed of 1000 mm/s. It will be apparent to those skilled in the art that many modifications and variations are possible in light of the above teaching.

[0072] FIG. 6D corresponds to process 522 and illustrates an embodiment in which an average value 618 for each scanlet is determined. In some embodiments, inputs to process 522 include total sample count 516, lasered part area 518, and scanlet data 520. Using these inputs, the average can be used to determine the area under the curve (AUC) as shown in equation (5):

Here, V is the average voltage determined for each scanlet and N is the number of samples. In FIG. 6D, the average voltage of the 20 sample segment corresponds to the integral of the signal since the width of the data is fixed.

[0073] FIG. 6E shows the lasered part area A _i for each scanlet 622 and the lasered part area for all scans 624. In addition to the area, we can calculate the scan length L _i and the sum L Sum _p of L _i over the part. L _i can be calculated using equation (6):

The x and y coordinates of the start and end of the scan can be specified or determined based on one or more direct sensor measurements.

[0074] FIG. 6F shows the rendered area 626 of the lasered part associated with the layers in the build plane. In some embodiments, once pdon _i , part area A _p , scan length L _i , and total length L Sum _p are determined, TED may be calculated using equation (7):

TED is sensitive to all user-defined laser powder bed fusion process parameters such as laser power, laser speed, and hatch spacing. TED values may be used for analysis using IPQM comparisons with baseline data sets. The resulting IPQM can be determined for each laser scan and displayed graphically or in three dimensions using a point cloud. FIG. 4G shows an exemplary graph. FIG. 4H shows an exemplary point cloud.

[0075] FIGS. 7A-7C show post-process porosity measurements and corresponding normalized in-process TED measurements. These figures show that in-process TED measurements can be accurate IPQM predictors of porosity and other manufacturing defects. FIG. 7A shows a comparison of TED metric data and a baseline dataset. The plot shows the value of each photodiode in the IPQM metric, both individually and in combination. On-axis photodiode data 702 can be obtained from a sensor aligned with the energy source. Off-axis photodiode data 704 may be collected by a sensor that is not aligned with the energy source. The combination of on-axis and off-axis photodiode data 706 provides the highest sensitivity to changes in process parameters. The x-axis 708 shows the build surface layer of the part and the y-axis 710 shows the Mahalanobis distance between the calculated TED and the baseline metric.

[0076] Mahalanobis distance can be used to standardize TED data. The Mahalanobis distance indicates how many standard deviations each TED measurement is from the normal distribution of TED measurements. In this case, the Mahalanobis distance indicates how many standard deviations each TED measurement is from the average TED measurement collected during the build of the control layers 526-600. The bottom chart of FIG. 7A also shows how TED varies with global energy density (GED) and porosity. In particular, in this series of experiments, a TED can be configured to predict the porosity of a part without destructive testing.

[0077] In some embodiments, quantitative metallographic characteristics (e.g., pore or intermetallic particle size and shape) and/or mechanical properties of the metal parts produced during the test run By comparing characteristics (eg, strength, toughness, or fatigue), the performance of the additive manufacturing device can be further verified. Generally, the presence of unfused metal powder particles within a test part indicates that not enough energy was applied, whereas a test part that receives too much energy can compromise the integrity of the fabricated part. Tends to form internal cavities. Porosity 714 may be representative of these defects.

[0078] In some embodiments, the nominal values used to generate FIG. 7A are obtained from previous testing. In some embodiments, the nominal value may also be obtained from subsequent testing since no calculations need to be made during the additive manufacturing operation. For example, if one wishes to compare the performance of two additive manufacturing devices, a nominal value can be determined by performing a test using the first additive manufacturing device. The performance of the second additive manufacturing device can then be compared to the nominal value determined by the first additive manufacturing device. In some embodiments where the performance of the two additive manufacturing devices is within a predetermined threshold of five standard deviations, comparable performance can be expected from the two machines. In some embodiments, the predetermined threshold may be a 95% statistical confidence level derived from an inverse chi-square distribution. This type of testing methodology can also be used to identify changes in performance over time. For example, after calibrating the machine, the results of the test pattern can be recorded. After a certain number of manufacturing operations have been performed by the device, the additive manufacturing device can be run again. An initial test pattern run after a calibration run can be used as a baseline to identify changes in additive manufacturing equipment performance over time. In some embodiments, settings of the additive manufacturing device can be adjusted to return the additive manufacturing device to post-calibration performance.

[0079] FIG. 7B shows the post-process metallography of a part built using an additive manufacturing process. FIG. 7B shows part 718 and a corresponding cross-section 720 of the part. Sections 1-11 correspond to sections 1-11 in FIG. 7A. The changes in process parameters, and the resulting changes in porosity, can be seen in the cross-sectional view 720 of the part. In particular, sections 2 and 3 have the highest porosity of 3.38% and 1.62%, respectively. The increased number of defect marks 722 on the sample portion indicates high porosity in the cross section.

[0080] FIG. 7C shows the IPQM results at the corresponding cross section determined during metallography. Each cross section includes an energy density 724 in units of J/mm ² and a porosity 714. The sample with the largest number of defective marks 722 corresponds to the TED measurement with the most standardized distance from the baseline. This plot shows that low standardized distances can predict dense and low porosity metallography, while high standardized Marajabonis distances are highly correlated with high porosity and poor metallography. It shows. For example, the low power settings used in creating the layers around layer 200 result in high porosity 714 and a large number of defect marks 722. In contrast, using the center load setting at or around the 600th layer, there are no discernible defect marks 722 and the lowest porosity value recorded is 0.06%.

[0081] FIG. 8 illustrates an alternative process in which data recorded by optical sensors, such as non-imaging photodetectors, can be processed to characterize additive manufacturing build processes. At 802, raw sensor data is received that can include both correlated build surface intensity data and energy source drive signals. At 804, individual scans may be identified and located within the build surface by comparing the drive signal and the build surface intensity data. Generally, the energy source drive signal provides at least a starting position and an ending position from which the range over which the scan can extend can be determined. At 806, raw sensor data related to the intensity or power of each scan may be binned into corresponding X&Y grid regions. In some embodiments, raw intensity or power data can be converted to energy units by correlating the dwell time of each scan in a particular grid region. In some embodiments, each grid area may represent one pixel of an optical sensor monitoring the build surface. Note that different coordinate systems can be used to store grid coordinates, such as polar coordinates, and storage of coordinates should not be limited to Cartesian coordinates. In some embodiments, different scan types can be binned separately so that analysis can be performed only on specific scan types. For example, an operator may want to focus on contour scans if these types of scans are most likely to contain unwanted variations. At 808, the energy input at each grid region may be summed so that the total amount of energy received at each grid region can be determined using equation (8).

[0082] This summation may be performed just before adding a new layer of powder to the build surface, or the summation may be delayed until a predetermined number of layers of powder have been deposited. For example, summation can only be performed after depositing and fusing 5 or 10 different powder layers during an additive manufacturing process. In some embodiments, the sintered layer of powder can add about 40 microns to the thickness of the part, but this thickness will vary depending on the type of powder used and the thickness of the powder layer.

[0083] At 810, the standard deviation of the samples detected and associated with each grid region is determined. This allows grid regions with small or large variations in power readings to be identified. A variation in standard deviation may indicate a problem with sensor performance or if one or more scans are missing or have power levels far above normal operating parameters. Standard deviation can be determined using equation (9).

[0084] At 812, the total energy density received in each grid region can be determined by dividing the power reading by the total area of the grid region. In some embodiments, the grid area can have a square shape with a length of about 250 microns. The energy density of each grid region may be determined using equation (10).

[0085] At 814, different grid regions may be associated with different parts if multiple parts are being built. In some embodiments, the system uses stored coordinates that can be used to quickly associate each grid area and its associated energy density to each part using the coordinates of the grid area and the boundaries associated with each part. can include defined part boundaries.

[0086] At 816, the area of each layer of the part may be determined. If the layer contains voids or serves to define internal cavities, a large portion of the layer may not receive energy. Therefore, the affected area can be calculated by summing only those grid areas that have been identified as receiving some amount of energy from the energy source. At 818, the total amount of power received by the grid regions within the portion of the layer associated with the component may be summed and divided by the affected area to determine the energy density of that layer of the component. Area and energy density may be calculated using equations (11) and (12).

[0087] At 820, the energy density of each layer may be summed to obtain a metric indicating the total amount of energy received by the part. The overall energy density of the part can then be compared to the energy density of other similar parts on the build surface. At 822, the total energy from each component is summed. This allows for high-level comparisons between different builds. Build comparisons can help identify systematic differences such as powder variations or changes in overall power output. Finally, at 824, the summed energy value can be compared to other layers, parts, or build surfaces to determine the quality of the other layers, parts, or build surfaces.

[0088] It should be appreciated that the specific steps shown in FIG. 8 provide a specific method of characterizing an additive manufacturing build process according to another embodiment of the invention. Other orders of steps may be performed according to alternative embodiments. For example, alternative embodiments of the invention may perform the above steps in a different order. Also, the individual steps shown in FIG. 8 may include multiple substeps that may be performed in various orders as appropriate for the individual steps. Additionally, additional steps may be added or removed depending on the particular application. Many variations, modifications, and substitutions will be recognized by those skilled in the art.

[0089] FIGS. 9A-9D show visual depictions of how multiple scans can contribute to the power introduced in individual grid regions. FIG. 9A shows a grid pattern comprised of multiple grid regions 902 distributed over a portion of a part being built by an additive manufacturing system. FIG. 9A also shows a first pattern of energy scans 904 extending diagonally across grid region 902. A first pattern of energy scans 902 may be applied by a laser or other intense source of thermal energy scans across grid 904. FIG. 9B shows how the energy introduced throughout the part is represented in each of the grid regions 902 by a single grayscale color representing the amount of energy received, with darker shades of gray corresponds to a larger amount of energy. Note that in some embodiments, the size of grid region 902 can be reduced to obtain higher resolution data. Alternatively, the size of grid area 902 can be increased to reduce memory and processing power usage.

[0090] FIG. 9C shows a second pattern of energy scans 906 that overlaps at least a portion of the energy scans of the first pattern of energy scans. As explained in the text accompanying Figure 8, if the first and second patterns of energy scans overlap, the grid area will appear darkly shaded and the energy of both scans will reflect the amount of energy received in the overlapping scan patterns. Show how it was increased. Obviously, this method is not limited to two overlapping scans, but can include many other additional scans that are added together to fully represent the energy received in each grid area.

[0091] FIG. 10A depicts an exemplary turbine blade 1000 suitable for use in the described embodiments. Turbine blade 1000 includes multiple different surfaces and includes multiple different features that require many different types of complex scans to generate. In particular, turbine blade 1000 includes a hollow blade portion 1002 and a tapered base portion 1004. FIG. 10B shows an exemplary manufacturing configuration in which 25 turbine blades 1000 can be manufactured simultaneously on a build surface 1006.

[0092] FIGS. 10C-10D show different cross-sectional views of different layers of the configuration shown in FIG. 10B with a grid TED-based visualization layer. FIG. 10C shows layer 14 of turbine blade 1000, where the TED-based visualization layer shows that the lower ends of selected ones of base portion 1004 are connected to a plurality of voids within turbine blades 1000-1, 1000-2, and 1000-3. This shows how it can be defined. Because the energy density data is associated with individual grid regions, these voids that would otherwise be completely hidden within the turbine blades are clearly visible in this grid TED-based visualization. FIG. 10D shows that the top of the base portion 1004 is clearly discernible from the gridded TED-based visualization layer shown in FIG. 10D. This shows how a void can be defined.

[0093] FIGS. 11A-11B illustrate cross-sectional views of base portions 1004-1 and 1004-2 of two different turbine blades 1000. FIG. 11A shows base portion 1004-1 manufactured using nominal manufacturing settings. The outer surfaces 1102 and 1104 of base portion 1004-1 receive substantially more energy than the interior 1106 of base portion 1004-1. The increased energy input to the outer surface provides a more uniform hardened surface, resulting in an annealing effect along the outer surfaces 1102 and 1104. This additional energy may be introduced using a high energy contour scan targeting the outer surfaces 1102 and 1104. FIG. 11B shows base portion 1004-2 produced with the same manufacturing settings as 1004-2, except that the contour scan is omitted. Although all scans utilized in the manufacturing operation to generate base portion 1004-2 were also included in the generation of base portion 1004-1, the energy density input for all scans covering each grid area is summed to The difference between base portions 1004-1 and 1004-2 can be clearly seen.

[0094] FIG. 11C shows the difference in surface consistency between base portions 1004-1 and 1004-2. Clearly, omitting the contour scan of the base portion 1004-2 has a substantial impact on the overall exterior quality. The outer surface of base portion 1004-1 is smoother, more consistent, and less porous. The annealing effect on base portion 1004-1 should also provide substantially greater strength than base portion 1004-2.

[0095] FIG. 12 shows the thermal energy density of parts associated with multiple different builds. Builds A through G each include thermal radiation data for approximately 50 different parts (represented by individual circles 1202) that were built during the same additive manufacturing operation. This figure shows how thermal radiation data can be used to track variations between different builds. For example, lots A, B, and C all have similar TED distributions, but building D, E, and F within tolerance results in consistently lower thermal radiation data. In some cases, these types of changes can be due to powder lot changes. In this way, thermal radiation data can be used to track systematic errors that can negatively impact overall output quality. Note that although this chart shows average TED values based on grid TED, similar charts can be created for scan-based TED methodologies.

[0096] FIGS. 13-14B illustrate examples of how thermal energy density can be used to control the operation of a part using field measurements. Figure 13 shows how parts are manufactured using different combinations of energy source power and scan speed and then destructively analyzed to determine the resulting part density in grams per cubic centimeter as shown. It shows. In this trial, a part associated with part density 1302 with a part density of 4.37 g/cc was manufactured using the manufacturer's recommended scan and laser power settings. Based on the resulting density readings, the location of dashed line 1304 can be determined. Line 1304 shows where, due to the resulting reduction in energy input, some of the powder is not heated enough to fuse and the part density falls below the threshold density level. Also, if too much energy is applied to the system, the density of the part can decrease and keyholes can form in the part as the powder vaporizes instead of melting during manufacturing operations. Dashed line 1306 can be determined experimentally from density data and is identified in this example by a lower part density of 4.33 g/cc. Line 1308 represents an optimal energy density contour where energy density and part shape remain substantially constant. The density test shows how the average density of parts made using settings distributed along line 1308 remains relatively consistent.

[0097] FIGS. 14A-14B illustrate thermal energy density contours overlaid on a portion of a power density graph and show that thermal density measurements collected during an additive manufacturing operation depend on the settings used by the energy source. The figure shows how things change. As shown in FIGS. 14A-14B, darker shading indicates higher thermal energy density and lighter shading indicates lower thermal energy density. From component density tests and thermal energy density profiles, control limits for specific components can be determined. In this case, control limits, indicated by ellipse 1402, have been determined and the power and scan rate parameters are used to produce part 1302 to 3σ along line 1308 and 1σ along a line perpendicular to line 1308. You can change the settings from the settings provided. In some embodiments, acceptable variations in power and speed allow in-process adjustments to be made to maintain a desired thermal energy density during a production run. Ellipse 1404 shows the entire process window that can accommodate further variations outside the control limits. In some embodiments, this process window can be used to identify variations that can validate the resulting part using only in-process data. Although the depicted control and process windows are shown as ellipses, other process window shapes and sizes are possible and may be more complex depending on the shape of the part and the eccentricity of the material, for example. Please note that. During manufacturing operations, optical sensor readings that measure thermal radiation can be used to determine thermal energy density in situ. If the thermal energy density falls outside of the expected range when maintaining the laser power and scan speed within the control limits indicated by ellipse 1402, the portion of the part with the abnormal thermal energy density reading may be defective. You can be warned that there is.

[0098] In some embodiments, a process window can be incorporated into a modeling and simulation program that models one or more optical sensors that collect sensor readings used to determine thermal energy density. can. Once the modeling and simulation system has been iterated to a first approximation of the workpiece's instruction set, the expected thermal energy density can be output to the additive manufacturing machine for additional testing. If the additive manufacturing machine includes optical sensors and computing equipment configured to measure thermal energy density, the modeled thermal energy density data can be used for further testing and validation on the additive manufacturing machine. . A comparison of modeled and measured thermal energy densities can be used to determine how well the instruction set's performance matches the expected results in situ.

[0099] FIG. 14C is another power density graph highlighting various physical effects due to setting the energy source too far from the process window 1404. For example, the power density graph shows that adding large amounts of laser power at low scan speeds creates keyholes in the workpiece. Keyhole formation occurs because some of the powder material evaporates by receiving too much energy. Additionally, low power combined with high scan speed may result in powder unfusion. Finally, high power levels combined with high scan speeds result in molten metal balling up during build operations. Note that varying the thickness of the deposited powder layer can result in a shift in the line separating the conduction mode zone from the keyhole formation zone and lack of a fusion zone. For example, increasing the thickness of the powder layer has the effect of increasing the slope of the line separating the keyhole from the conduction mode zone and the lack of a molten region, since generally thicker layers require more energy to undergo liquefaction. There is.

[0100] FIG. 14C also illustrates how power and scan rate settings corresponding to the conduction mode zone generally do not result in any of the critical deficiencies described above, but lasers corresponding to values within process window 1404. By maintaining the settings, the resulting grain structure and/or part density can be optimized. Another advantage of maintaining energy source settings within process window 1404 is that these settings are required to maintain thermal energy density readings within a narrow range. If the value of thermal energy density is outside of a predetermined range during manufacturing operations, it may indicate a problem with the manufacturing process. These issues can be addressed by moving the settings closer to the center area of the process window or by updating the process window for subsequent parts. In some embodiments, the manufacturer may discover that the failure was caused by defective powder or other rare aberrations that should not be accounted for in subsequent manufacturing processes. It is to be understood that the process window drawn is not the same for all parts of the part and can vary significantly depending on which areas or specific layers of the part were being worked on at a particular time. The size and/or shape of process window 1404 may also vary according to other factors, such as hatch spacing, scan length, and scan direction, in some embodiments.

[0101] FIG. 14D shows how the size and shape of the melt pool can vary according to laser power and scan speed settings. Example melt pools 1406-1418 illustrate example melt pool sizes and shapes for various laser power and speed settings. It can be seen that, in general, larger melt pools result from higher amounts of laser power and lower scan speeds. However, in this particular configuration, the size of the melt pool depends more on laser power than on velocity.

[0102] FIGS. 15A-15F illustrate how grids can be dynamically created to characterize and control additive manufacturing operations. FIG. 15A shows a top view of a cylindrical workpiece 1502 positioned over a portion of a build surface 1504. Workpiece 1502 is shown undergoing an additive manufacturing operation. FIG. 15B shows how the cylindrical workpiece 1502 is divided into a plurality of tracks 1506 along which an energy source can melt the powder dispersed on the top surface of the cylindrical workpiece 1502. In some embodiments, the energy source can alternate directions 1506 as shown, and in other embodiments, the energy source can always move in one direction. Additionally, the direction of the tracks 1506 may vary from layer to layer to further randomize the direction of the scan used to build the workpiece 1502.

[0103] FIG. 15C shows an example scan pattern of an energy source as it forms a portion of workpiece 1502. As indicated by arrow 1508, the direction of movement of the exemplary energy source across workpiece 1502 is diagonal. Each scan 1510 of the energy source is oriented perpendicular to the direction of movement of the energy source along the track 1506 and can completely traverse the track 1506. The energy source can be turned off for short periods between successive individual scans 1510. In some embodiments, the duty cycle of the energy source may be approximately 90% as it traverses each of the tracks 1506. By employing this type of scanning strategy, the energy source can cover the width of track 1506 as it traverses workpiece 1502. In some embodiments, swath 1510 can have a width of about 5 mm. In some embodiments, the width of the melt pool produced by the energy source can be on the order of about 80 microns, which can substantially reduce the number of tracks needed to form the workpiece 1502. can.

[0104] FIGS. 15D-15E illustrate how grid regions 1512 are dynamically generated along each track 1506 and sized to correspond to the width of an individual scan 1510. The system can predict the exact location of subsequent scans by reference to the energy source drive signal towards the energy source. In some embodiments, the width of the grid 1512 may match the length of the individual scans 1512 or be within 10% or 20% of the length of the individual scans 1512. Again, by reference to the energy source drive signal, the scan length of an individual scan 1512 can be predicted. In some embodiments, grid area 1512 may be square or rectangular in shape. As the energy source continues along the track 1506, the thermal energy density can be determined for each of the grid regions 1512. In some embodiments, the thermal energy density reading in grid region 1512-1 can be used to adjust the output of the energy source in the next grid region, in this case grid region 1512-2. For example, if the thermal energy density readings produced by individual scans 1510 within grid region 1512-1 are significantly higher than expected, the power output of the energy source may be reduced and the energy source across individual scans 1510 The speed of the scan can be increased and/or the spacing between individual scans 1510 within grid area 1512-2 can be increased. These adjustments can be made as part of a closed loop control system. Although only five individual scans 1510 are shown within each region, this is done for illustrative purposes only and the actual number of individual scans within grid region 1512 can be substantially higher. . For example, if the width of the melt zone produced by the energy source is approximately 80 microns, it would take approximately 60 individual scans 1510 for all the powder within a 5 mm square grid area 1512 to fall within the melt zone. It may be necessary.

[0105] FIG. 15F shows the edge region of the workpiece 1502 after the energy source has finished traversing the pattern of tracks 1506. In some embodiments, the energy source can continue to add energy to the workpiece 1502 after most of the powder has melted and resolidified. For example, contour scan 1514 may be traced along an outer circumference 1516 of workpiece 1502 to apply a surface finish to workpiece 1502. It should be appreciated that the illustrated contour scan 1514 is substantially shorter than the individual scans 1510. Therefore, grid area 1518 can be much narrower than grid area 1512. It should also be noted that grid area 1518 is not purely rectangular in shape, as it follows the contour of the outer perimeter of workpiece 1502 in this case. Another example where differences in scan length can occur is when the workpiece contains walls of varying thickness. Varying wall thickness can result in varying scan lengths within a single grid area. In such cases, the area of each grid region can be kept consistent by increasing the length of the grid region while decreasing its width as the length of each individual scan changes.

[0106] FIG. 16 illustrates a closed loop control example illustrating a feedback control loop 1600 for establishing and maintaining feedback control of additive manufacturing operations. At block 1602, the baseline thermal energy density of the next grid area that the energy source is attempting to traverse is input into the control loop. This baseline thermal energy density reading can be established from modeling and simulation programs and/or from previously performed experiments/test runs. In some embodiments, this baseline thermal energy density data may be adjusted by an energy density bias block 1604 that includes energy density readings for various grid regions recorded during previous layers. Energy density bias block 1604 may include adjustments to the baseline energy density block if the previous layer receives too much or too little energy. For example, if an optical sensor reading indicates a lower-than-nominal thermal energy density in one area of the workpiece, the energy density bias value is set to the baseline of the grid area that overlaps the grid area that is below the nominal thermal energy density reading The value of energy density can be increased. In this way, the energy source can fuse additional powder that was not completely fused between the previous layer or layers.

[0107] FIG. 16 also illustrates how inputs from blocks 1602 and 1604 work together to generate an energy density control signal that is received by controller 1606. Controller 1606 is configured to receive the energy density control signal and generate heat source input parameters configured to produce a desired thermal energy density within the current grid area. Input parameters can include power, scan speed, hatch spacing, scan direction, and scan time. The input parameters are then received by the energy source 1608 and changes in the input parameters are adopted by the energy source 1608 in the current grid region. Once the optical sensor measures the scan of the energy sources 1608 that make up the current grid area, the thermal energy density of the current grid area is calculated and compared to the energy density control signal at block 1610. If the two values are the same, the energy density control signal is not changed based on the optical sensor data. However, if the two values are different, the difference is added or subtracted from the energy density control signal of the scan performed in the next grid area.

[0108] In some embodiments, the grid area of the current layer and all previous layers is a dynamically generated grid oriented according to the path and scan length/width of the scan performed by the energy source. It can be an area. In this type of configuration, both the baseline energy density and the energy density bias may be based on dynamically generated grid regions. In other embodiments, the grid area for the current layer can be generated dynamically, and the energy density bias data 1604 is the energy density reading associated with the static grid area defined before the start of the additive manufacturing operation. It may be based on a value, so that the static grid area remains fixed across the part and does not change in position, size, or shape. The grid may be uniformly shaped and spaced if a Cartesian grid system is desired, but it may also take the form of grid areas that constitute a polar grid system. In other embodiments, the current layer's grid area is statically generated before the build operation is performed, and the energy density bias data is also statically generated and shares the same grid used for the current layer. can.

[0109] In some embodiments, thermal radiation density may be used in place of thermal energy density in control loop 1600. Thermal radiation density may refer to other factors in addition to thermal energy density. For example, thermal radiation density is measured along with one or more other characteristics such as peak temperature, minimum temperature, heating rate, cooling rate, average temperature, standard deviation from average temperature, rate of change of average temperature over time, etc. , can be a weighted average of multiple features, including thermal energy density. In other embodiments, one or more other features may be used to verify that the scans comprising each grid region reach a desired temperature, heating rate, or cooling rate. In such embodiments, the validation feature may serve as a flag indicating that input parameters of the energy source may need to be adjusted within a defined control window to achieve a desired temperature, heating rate, or cooling rate. Can be used. For example, if the peak temperature within the grid region is too low, the power can be increased or the scan speed can be decreased. Note that the foregoing description of control loop 1600 in connection with various types of grid TEDs will also be understood by those skilled in the art that scan TED metrics can also be used in similar loop configurations.
TED analysis of recoater arm short feed

[0110] FIG. 17A shows a normal distribution of powder 1702 across build plate 1704. This depiction shows how the powder spreads evenly without height variations. In contrast, FIG. 17B shows how the thickness of the powder layer 1702 can vary significantly when an insufficient amount of powder 1702 is collected by the recoater arm. As the recoater arm begins to run out of powder 1702, the thickness of the layer of powder 1702 gradually tapers off until a portion of build plate 1704 is completely bare of powder 1702. Early detection of this type of phenomenon is important for accurate defect detection, as this type of error can have a very negative impact on the overall quality of the part.

[0111] FIG. 17C is a black and white photograph of the build plate where a short feed of powder 1702 only partially covers the nine workpieces placed on the build surface. In particular, the three workpieces on the right side of the photo are completely covered, while the three workpieces on the left side are almost completely uncovered.

[0112] FIG. 17D shows how the detected thermal energy density differs substantially when the energy source scans across all nine workpieces using the same input parameters. Region 1706 produces a substantially higher thermal energy density reading because the emissivity of the powder is substantially higher and the thermal conductivity of the build plate or solidified powder material is higher than the effective thermal conductivity of the powder. High thermal conductivity reduces the amount of energy available for radiation back to the optical sensor, thereby reducing the detected thermal radiation. Additionally, the amount of thermal energy radiated is also reduced due to the low emissivity of the material itself.
Thermal energy density and global energy density

[0113] When power supplied from an energy source is provided to the workpiece, the material making the workpiece melts, but that power is also consumed by several other heat and mass transfer processes during the additive manufacturing process. can be done. The following equation describes the various processes that can absorb the emitted power when the energy source is a laser scan across a powder bed.
P _{TOTAL LASER POWER} =P _{OPTICAL LOSSES AT THE LASER} +P _{ABSORPTION BY CHAMBER GAS} +P _REFLECTION +P _{PARTICLE AND PLUME INTERAC TIONS} +P _{POWER NEEDED TO SUSTAIN MELT POOL} +P _{CONDUCTION LOSSES} +P _{RADIATION LOSSES} +P _{CONVECTION LOSSES} +P _{VAPORIZATION LO SSES} formula (13)

[0114] OPTICAL LOSSES AT THE LASER refers to power losses due to imperfections in the optical system that transmits and focuses the laser light at the build surface. Imperfections result in absorption and reflection losses of the emitted laser within the optical system. ABSORPTION BY CHAMBER GAS refers to the power loss due to gases in the build chamber of an additive manufacturing system that absorb a small portion of the laser power. The effect of this power loss depends on the absorption rate of the gas at the laser wavelength. Reflection loss refers to the power lost due to light escaping the laser light that is never absorbed by the powder bed. PARTICLE AND PLUME INTERACTIONS refers to the interaction between the laser and the plume and/or the particles emitted during the deposition process. Power loss due to these effects can be ameliorated by circulating a shielding gas through the build chamber, but usually a small amount of power loss cannot be completely avoided. POWER NEEDED TO SUSTAIN MELT POOL is the amount of power absorbed by the working material to melt the powder and ultimately superheat it to the temperature finally achieved by the melt pool. Refers to the part of the laser power that is used. CONDUCTION LOSSES refers to the portion of power absorbed by conduction into the solidified metal beneath the powder and into the powder bed itself. In this way, the powder bed and solidified material that make up the part conduct heat from the molten pool. This conductive transfer of thermal energy is the primary form of energy loss from the melt pool. RADIATION LOSSES refers to the portion of the laser power emitted from the melt pool and the material surrounding the melt pool that is hot enough to emit thermal radiation. CONVECTION LOSSES refer to losses caused by the transfer of thermal energy to gases circulating through the build chamber. Finally, VAPORIZATION LOSSES refers to the small portion of the powder material that evaporates under laser irradiation. Since the latent heat of vaporization is very large, this is a strong cooling effect on the melt pool and can cause non-negligible energy losses when the total power of the laser beam is high.

[0115] Thermal Energy Density (TED) metric is the result of the emission of light from a heated area, the transmission of this light through an optical system, the collection of light by a detector, and the conversion of this light into an electrical signal. is based on optical light measurements. The equation governing blackbody radiation over all possible frequencies is given by the Stefan Boltzmann equation shown in equation (14) below:

[0116] The variables in equation (14) are shown in Table (1) below.

[0117] There are additional intervening factors that affect the emitted light before it is collected by the sensor and thus becomes the voltage used to calculate the TED metric. This is summarized below in equation (15).
V _{VOLTAGE USED BY TED} = {P _RADIATED -P _{VIEW FACTOR} -P _{OPTICAL LOSSES AT RADIATED WAVELENGTHS} -P _{SENSOR LOSS FACTOR} }*(SE NSOR SCALING FACTOR) formula (15)

[0118] These various terms from Equation (15) are explained in Table (2) below.

[0119] A figure of merit often used in additive manufacturing is global energy density (GED). This is a parameter that combines various PROCESS INPUTs, as shown in equation (16) below:
GED=(BEAM POWER)/{(TRAVEL SPEED)*(HATCH SPACING)} Formula (16)

[0120] The energy units per unit area of GED are: (JOULES/SEC)/{(CM/SEC)*(CM)}=JOULES/ ^CM2 . However, it should be noted that although GED may have the same units as TED, GED and TED are generally not equivalent. As an example, TED is obtained by dividing the radiated power from a hot region by the area, while GED is a measurement of input power. As described herein, TED relates to RESPONSE or PROCESS OUTPUT, and GED relates to PROCESS INPUT. The inventor believes that, as a result, TED and GED are different measurements from each other. In some embodiments, the area utilized in determining TED is different than the melt pool area. As a result, some embodiments do not have a direct correlation between TED and melt pool area. Preferably, TED is sensitive to various factors that directly affect additive manufacturing processes.

[0121] Although embodiments described herein use data generated by optical sensors to determine thermal energy density, embodiments described herein use data generated by optical sensors to determine thermal energy density. It may be implemented using data generated by sensors that measure symptoms. Sensors that measure manifestations of in-process physical variables include, for example, force and vibration sensors, contact thermal sensors, non-contact thermal sensors, ultrasonic sensors, and eddy current sensors. It will be apparent to those skilled in the art that many modifications and variations are possible in light of the above teaching.

[0122] Various aspects, embodiments, implementations or features of the described embodiments may be used separately or in any combination. Various aspects of the described embodiments may be implemented by software, hardware, or a combination of hardware and software. The described embodiments may also be implemented as computer readable code on a computer readable medium for controlling manufacturing operations or as computer readable code on a computer readable medium for controlling a manufacturing line. A computer-readable medium is any data storage device that can store data that can then be read by a computer system. Examples of computer readable media include read only memory, random access memory, CD-ROM, HDD, DVD, magnetic tape, and optical data storage devices. The computer-readable medium can also be distributed over network-coupled computer systems so that the computer-readable code is stored and executed in a distributed manner.

[0123] The foregoing description has used specific terminology for purposes of explanation to provide a thorough understanding of the described embodiments. However, it will be apparent to those skilled in the art that the specific details are not required to implement the illustrated embodiments. Accordingly, the foregoing descriptions of specific embodiments have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the described embodiments to the precise forms disclosed. It will be apparent to those skilled in the art that many modifications and variations are possible in light of the above teaching.

Claims (20)

depositing a layer of powder metal on the build surface;
dividing at least a portion of each build surface into a plurality of grid regions, each grid region having a respective grid area;
generating multiple scans over each grid area using an energy source to fuse the layers of powdered metal;
determining an amount of energy emitted from each grid region while each of the plurality of scans for each grid region is performed;
determining a thermal energy density for each grid region;
comparing the thermal energy density of each grid region with a reference value to identify potentially defective grid regions;
additive manufacturing methods for producing components, including; 2. The method of claim 1, further comprising representing potentially defective grid regions using a graphical medium. 2. The method of claim 1, further comprising representing potentially defective grid regions using text-based media. The method of claim 1, wherein the energy source is a laser, and in response to determining that a grid area has a potential defect, adjusting subsequent scans of the laser to repair the grid area. . 2. The method of claim 1, wherein identifying the potentially defective grid regions includes classifying each grid region of the plurality of grid regions as nominal or non-nominal. 6. The method of claim 5, wherein each non-nominal grid region is classified based on a set of non-nominal thresholds. dividing the build surface into a plurality of grid regions, each of the grid regions having a respective grid area;
generating multiple scans of an energy source across the build surface;
determining the total amount of energy emitted from each grid region during the plurality of scans;
calculating a thermal energy density associated with each grid area of the plurality of grid areas based on the total amount of energy radiated from each grid area and the grid area of each grid area;
comparing the thermal energy density calculated for each grid region of the plurality of grid regions with a reference energy density value to identify potentially defective grid regions;
additive manufacturing methods, including; 8. The method of claim 7, further comprising representing potentially defective grid regions using a graphical medium. 8. The method of claim 7, further comprising representing potentially defective grid regions using text-based media. 8. The method of claim 7, wherein the energy source is a laser, and in response to determining that the grid area has a potential defect, adjusting subsequent scans of the laser to repair the grid area. . 8. The method of claim 7, wherein identifying the potentially defective grid regions includes classifying each grid region of the plurality of grid regions as nominal or non-nominal. 12. The method of claim 11, wherein each non-nominal grid region is further classified based on a set of non-nominal thresholds. 8. The method of claim 7, wherein the thermal energy density of each grid region is determined by dividing the total amount of energy radiated from each grid region by the grid area of that grid region. depositing a layer of powder metal on the build surface;
fusing the layers of powdered metal using an energy source to generate multiple scans across the build surface;
measuring the amount of energy radiated from the build surface during the plurality of scans;
determining a thermal energy density of the build surface based on the amount of energy radiated and the area of the build surface traversed by the plurality of scans;
determining whether there are potential defects in the build surface by comparing the thermal energy density with a reference thermal energy density value;
Additive manufacturing methods for manufacturing components, including: 15. The method of claim 14, further comprising dividing the build surface into a plurality of grid regions, each grid region having a respective grid area. 10. The method of claim 1, further comprising: determining a thermal energy density for each grid region; and comparing the determined thermal energy density with a reference thermal energy density to determine whether there is a potential defect in the grid region. 15. The method described in 15. 15. The method of claim 14, further comprising representing potentially defective grid regions using a graphical medium. 15. The method of claim 14, further comprising representing potentially defective grid regions using text-based media. 15. The method of claim 14, wherein the energy source is a laser, and in response to determining that the build surface has a potential defect, adjusting subsequent scans of the laser to repair the build surface. . 15. The method of claim 14, wherein the thermal energy density of the build surface is determined by dividing the amount of energy radiated from the build surface by the area of the build surface traversed by the plurality of scans.